State & Territory-Level Mapping
Prepare Data
To use this package, you will need to have a data frame with two columns:
- a categorical variable coded as a factor
- the two-letter US Postal Service code for each state and territory
(e.g., VA for Virginia or VI for Virgin Islands).
fipscodes.rdais provided to facilitate #2.
For this vignette, we’ll be using the two provided datasets
census.uninsured19 and cdc.cvd.
census.uninsured19 provides an example of a dataset with
complete data for all 50 states, D.C., and the 5 US territories. It is
already in the proper format for the provided package functions.
cdc.cvd is missing values for territories and requires
additional processing which we will demonstrate below.
We can see that census.uninsured19 has these two
components: 1. “Percent.Cat”: the Percentage Ages 19 or Under with No
Health Insurance categorized as a factor 2. “STUSPS”: the two letter US
Postal Service code
In cdc.cvd we are missing the territories and our fill
variable (Data_Value) has not yet been prepared as a factor.
We’ll first join the dataset to the provided fips_code
dataset to get the full list of jurisdictions. Then we’ll code a new
factor variable for mapping.
data("fips_codes_state")
cdc.cvd <- fips_codes_state %>%
left_join(cdc.cvd, by = c("state" = "LocationAbbr")) %>%
mutate(data.cat = factor(
case_when(
Data_Value < 198 ~ "Q1 (166 to < 198)",
Data_Value >= 198 & Data_Value < 215 ~ "Q2 (198 to < 215)",
Data_Value >= 215 & Data_Value < 248 ~ "Q3 (215 to < 248)",
Data_Value >= 248 & Data_Value < 400 ~ "Q4 (248 to 326)",
is.na(Data_Value) ~ "Data Not Available"
),
levels = c("Q1 (166 to < 198)", "Q2 (198 to < 215)",
"Q3 (215 to < 248)", "Q4 (248 to 326)", "Data Not Available")
)
)
table(cdc.cvd$data.cat)
#>
#> Q1 (166 to < 198) Q2 (198 to < 215) Q3 (215 to < 248) Q4 (248 to 326)
#> 13 13 12 13
#> Data Not Available
#> 6Mapping US with Territory Geometries
Using Census Insurance Data
Start by defining the fill category colors with their factor labels.
colors.census <- c("Less than 5%" = "#feebe2",
"5% to <10%" = "#f768a1",
"10% or Greater" = "#7a0177")Then specify any required parameters of the function (see documentation for details).
map1_categorical(data = census.uninsured19,
join_var = "STUSPS",
fill_var = "Percent.Cat",
fill_color = colors.census,
legend_name = "Percent Uninsured",
territory_label_color = "black",
title = "Figure 1. Percent Uninsured, Ages <19 Years",
save.filepath = "saved-maps/map1-uninsure.png")
Let’s say we wanted to add a border to highlight specific states or territories. We’ll first define a vector of US postal service IDs (in this example, Oregon, Wisconsin, Virginia, and USVI) and then feed this into the border_ids parameter.
border <- c("OR", "WI", "VA", "VI")
map1_categorical(data = census.uninsured19,
join_var = "STUSPS",
fill_var = "Percent.Cat",
fill_color = colors.census,
legend_name = "Percent Uninsured",
title = "Figure 1. Percent Uninsured, Ages <19 Years",
border_ids = border,
border_color = "red",
border_linewidth = 1,
save.filepath = "saved-maps/map1-uninsure2.png")
Using CDC Cardiovascular Disease Mortality Data
Sometimes we may want to remove the inset box outline, which we can
do by specifying inset_box_color = "white".
We also highlight an additional option of removing the territory
labels by specifying territory_label_color = "white".
colors.cdc <- c("Q1 (166 to < 198)" = "#ffffcc",
"Q2 (198 to < 215)" = "#a1dab4",
"Q3 (215 to < 248)" = "#41b6c4",
"Q4 (248 to 326)" = "#225ea8",
"Data Not Available" = "grey80")
map1_categorical(data = cdc.cvd,
join_var = "state",
fill_var = "data.cat",
fill_color = colors.cdc,
fill_linewidth = 1.2,
fill_linecolor = "black",
inset_box_color = "white",
territory_label_color = "white",
legend_name = "CVD Mortality Rate\nper 100,000 persons",
border_ids = border,
border_color = "red",
border_linewidth = 1.5,
save.filepath = "saved-maps/map1-cvd.png") 
